Determination of defect location for examination of a specimen

ABSTRACT

There is provided a method and a system configured to obtain an image of a one or more first areas of a semiconductor specimen acquired by an examination tool, determine data D att  informative of defectivity in the one or more first areas, determine one or more second areas of the semiconductor specimen for which presence of a defect is suspected based at least on an evolution of D att , or of data correlated to D att , in the one or more first areas, and select the one or more second areas for inspection by the examination tool.

TECHNICAL FIELD

The presently disclosed subject matter relates, in general, to the fieldof examination of a specimen, and more specifically, to automating theexamination of a specimen.

BACKGROUND

Current demands for high density and performance associated with ultralarge scale integration of fabricated devices require submicronfeatures, increased transistor and circuit speeds, and improvedreliability. Such demands require formation of device features with highprecision and uniformity, which, in turn, necessitates carefulmonitoring of the fabrication process, including automated examinationof the devices while they are still in the form of semiconductor wafers.

Examination processes are used at various steps during semiconductorfabrication to detect and classify defects on specimens. Effectivenessof examination can be increased by automatization of process(es) as, forexample, Automatic Defect Classification (ADC), Automatic Defect Review(ADR), etc.

GENERAL DESCRIPTION

In accordance with certain aspects of the presently disclosed subjectmatter, there is provided a system of examination of a semiconductorspecimen, the system comprising a processor and memory circuitry (PMC)configured to obtain an image of one or more first areas of asemiconductor specimen acquired by an examination tool, determine dataD_(att) informative of defectivity in the one or more first areas,determine one or more second areas of the semiconductor specimen forwhich presence of a defect is suspected based at least on an evolutionof D_(att), or of data correlated to D_(att), in the one or more firstareas, and select the one or more second areas for inspection by theexamination tool.

According to some embodiments, determination of the one or more secondareas is based on a search of an extremum of D_(att).

According to some embodiments, the system is configured to determineD_(correl) representative of a correlation between data D_(att)informative of defectivity in the one or more first areas, and datarepresentative of a thickness of the semiconductor specimen in the oneor more first areas, determine one or more second areas of thesemiconductor specimen for which presence of a defect is suspected,wherein the one or more second areas are determined based on D_(correl),or data representative thereof, and data representative of a thicknessof the semiconductor specimen, and select the one or more second areasfor inspection by the examination tool.

According to some embodiments, the system is configured to performrepeatedly (1), (2), (3) and (4), from i equal to 1, until a stoppingcriterion is met: (1) obtain an image of one or more areas A_(i) of asemiconductor specimen acquired by an examination tool, (2) determinedata D_(att) informative of defectivity in the one or more areas A_(i),(3) determine one or more areas A_(i+1) of the semiconductor specimenfor which presence of a defect is suspected, based at least on anevolution of D_(att) in the one or more areas A_(i), and (4) revertingto (1) for i incremented by one.

According to some embodiments, the system is configured to generate foreach of a plurality of subsets of pixels present in the image of thesemiconductor specimen, a probability that a defect is present at eachsubset, wherein the second area is selected based at least on theprobability, wherein the probability is based on at least one of and (i)and (ii): (i) data D_(att) informative of defectivity in the one or morefirst areas; and (ii) data D_(correl) representative of a correlationbetween data D_(att) informative of defectivity in the one or more firstareas and data representative of a thickness of the semiconductorspecimen in the one or more first areas, and data representative of athickness of the semiconductor specimen.

According to some embodiments, data D_(att) in the one or more firstareas includes at least one of: data representative of a shape ofelements present in the one or more first areas, and data representativeof a difference between elements present in the one or more first areasand elements present in a reference image of the one or more firstareas.

According to some embodiments, if data D_(correl) includes a function Fwhich depends on data representative of a thickness of the semiconductorspecimen over a range R, the system is configured to select the secondarea such that at least one of (i), (ii) and (iii) is met:

-   -   (i) data representative of a thickness of the semiconductor        specimen in the second area complies with the function F in the        range R;    -   (ii) data representative of a thickness of the semiconductor        specimen in the second area allows testing the function F over a        range R′ different from R; and    -   (iii) data representative of a thickness of the semiconductor        specimen in the second area is selected to attempt to move        towards an extremum of an output of the function F.

According to some embodiments, at least one of data representative of athickness of the semiconductor specimen in the one or more first areas,and data representative of a thickness of the semiconductor specimen, isobtained based on pixel intensity in an image acquired by at least oneoptical examination tool.

According to some embodiments, at least one of data representative of athickness of the semiconductor specimen in the one or more first areasand data representative of a thickness of the semiconductor specimen isobtained based on pixel intensity in a plurality of images acquired byat least one optical examination tool, wherein the plurality of imagesdiffer by a wavelength of an illuminating optical signal of the opticalexamination tool.

According to some embodiments, the system is configured to select theone or more first areas based on a first probability map representingprobability of a presence of defects over the semiconductor specimen,wherein the first probability map is built based on at least one of animage of the semiconductor specimen acquired by an optical examinationtool, estimation of defect location based on an image of thesemiconductor specimen acquired by an optical examination tool,historical data regarding defect location, an image of the semiconductorspecimen acquired by an electron beam examination tool, a simulatedimage of the semiconductor specimen, a synthetic image of thesemiconductor specimen, and manufacturing data of the semiconductorspecimen.

According to some embodiments, at least one of data representative of athickness of the semiconductor specimen in the one or more first areasand data representative of a thickness of the semiconductor specimen, isobtained based on pixel intensity in an image acquired by at least oneoptical examination tool.

According to some embodiments, at least one of data representative of athickness of the semiconductor specimen in the one or more first areasand data representative of a thickness of the semiconductor specimen isobtained based on pixel intensity in a plurality of images acquired byat least one optical examination tool, wherein the plurality of imagesdiffer by a wavelength of an illuminating optical signal of the opticalexamination tool.

In accordance with certain aspects of the presently disclosed subjectmatter, there is provided a method of examination of a semiconductorspecimen, the method comprising, by a processor and memory circuitry(PMC): obtaining an image of one or more first areas of a semiconductorspecimen acquired by an examination tool, determining data D_(att)informative of defectivity in the one or more first areas, determiningone or more second areas of the semiconductor specimen for whichpresence of a defect is suspected based at least on an evolution ofD_(att), or of data correlated to D_(att), in the one or more firstareas, and selecting the one or more second areas for inspection by theexamination tool.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a computer, cause thecomputer to perform the method above.

According to some embodiments, the proposed solution allows efficientexamination of a specimen including small structures, for which signalto noise ratio (in an image acquired e.g. by an optical examinationtool) is low. According to some embodiments, the proposed solution copeswith contradictory requirements: efficient use of a low-speedhigh-resolution examination tool for examination of a specimen (thistool is required in particular for examination of small structures, butcan inspect only a small area of the specimen in a reasonable time), ina limited time and budget. According to some embodiments, the proposedsolution allows directing an examination tool in a smart and efficientway towards relevant areas of a specimen. According to some embodiments,the proposed solution optimizes time and cost required for examinationof a specimen.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the disclosure and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 illustrates a generalized block diagram of an examination systemin accordance with certain embodiments of the presently disclosedsubject matter.

FIG. 2 illustrates a generalized flow-chart of a method of building afirst probability map informative of locations of interest in thespecimen.

FIG. 3 illustrates a generalized flow-chart of a method of determininglocations of interest in the specimen for inspection by an examinationtool.

FIG. 3A illustrates a non-limitative example of a use of the method ofFIG. 3 on a specimen.

FIG. 4 illustrates a generalized flow-chart of an iterative method ofdetermining locations of interest in the specimen for inspection by anexamination tool.

FIG. 5 illustrates a generalized flow-chart of another possibleembodiment of the method of FIG. 3.

FIG. 5A illustrates a generalized flow-chart of a method of determiningdata representative of thickness of a specimen based on a plurality ofimages which differ by at least one acquisition parameter.

FIG. 5B illustrates an example of a function correlating data indicativeof a presence of a defect in an area and pixel intensity (which isrepresentative of thickness).

FIG. 5C illustrates another example of a function correlating dataindicative of a presence of a defect in an area and pixel intensity(which is representative of thickness).

FIG. 6 illustrates a generalized flow-chart of the method of FIG. 5applied iteratively.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the disclosure.However, it will be understood by those skilled in the art that thepresently disclosed subject matter may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the presently disclosed subject matter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “obtaining”,“selecting”, “determining”, “generating”, “outputting” or the like,refer to the action(s) and/or process(es) of a computer that manipulateand/or transform data into other data, said data represented asphysical, such as electronic, quantities and/or said data representingthe physical objects. The term “computer” should be expansivelyconstrued to cover any kind of hardware-based electronic device withdata processing capabilities including, by way of non-limiting example,the system 103 and respective parts thereof disclosed in the presentapplication.

The terms “non-transitory memory” and “non-transitory storage medium”used herein should be expansively construed to cover any volatile ornon-volatile computer memory suitable to the presently disclosed subjectmatter.

The term “specimen” used in this specification should be expansivelyconstrued to cover any kind of wafer, masks, and other structures,combinations and/or parts thereof used for manufacturing semiconductorintegrated circuits, magnetic heads, flat panel displays, and othersemiconductor-fabricated articles.

The term “examination” used in this specification should be expansivelyconstrued to cover any kind of metrology-related operations as well asoperations related to detection and/or classification of defects in aspecimen during its fabrication. Examination is provided by usingnon-destructive examination tools during or after manufacture of thespecimen to be examined. By way of non-limiting example, the examinationprocess can include runtime scanning (in a single or in multiple scans),sampling, reviewing, measuring, classifying and/or other operationsprovided with regard to the specimen or parts thereof using the same ordifferent inspection tools. Likewise, examination can be provided priorto manufacture of the specimen to be examined and can include, forexample, generating an examination recipe(s) and/or other setupoperations. It is noted that, unless specifically stated otherwise, theterm “examination” or its derivatives used in this specification are notlimited with respect to resolution or size of an inspection area. Avariety of non-destructive examination tools includes, by way ofnon-limiting example, scanning electron microscopes, atomic forcemicroscopes, optical inspection tools, etc.

By way of non-limiting example, run-time examination can employ a twophase procedure, e.g. inspection of a specimen followed by review ofsampled locations of potential defects. During the first phase, thesurface of a specimen is inspected at high-speed and relativelylow-resolution. In the first phase, a defect map is produced to showsuspected locations on the specimen having high probability of a defect.During the second phase at least part of the suspected locations aremore thoroughly analyzed with relatively high resolution. In some casesboth phases can be implemented by the same inspection tool, and, in someother cases, these two phases are implemented by different inspectiontools.

The term “defect” used in this specification should be expansivelyconstrued to cover any kind of abnormality or undesirable feature formedon or within a specimen.

The term “design data” used in the specification should be expansivelyconstrued to cover any data indicative of hierarchical physical design(layout) of a specimen. Design data can be provided by a respectivedesigner and/or can be derived from the physical design (e.g. throughcomplex simulation, simple geometric and Boolean operations, etc.).Design data can be provided in different formats such as, by way ofnon-limiting examples, GDSII format, OASIS format, etc. Design data canbe presented in vector format, grayscale intensity image format, orotherwise.

It is appreciated that, unless specifically stated otherwise, certainfeatures of the presently disclosed subject matter, which are describedin the context of separate embodiments, can also be provided incombination in a single embodiment. Conversely, various features of thepresently disclosed subject matter, which are described in the contextof a single embodiment, can also be provided separately or in anysuitable sub-combination. In the following detailed description,numerous specific details are set forth in order to provide a thoroughunderstanding of the methods and apparatus.

Bearing this in mind, attention is drawn to FIG. 1 illustrating afunctional block diagram of an examination system in accordance withcertain embodiments of the presently disclosed subject matter. Theexamination system 100 illustrated in FIG. 1 can be used for examinationof a specimen (e.g. of a wafer and/or parts thereof) as a part of thespecimen fabrication process. The illustrated examination system 100comprises computer-based system 103 capable of automatically determiningmetrology-related and/or defect-related information using imagesobtained during specimen fabrication. System 103 can be operativelyconnected to one or more low-resolution examination tools 101 and/or oneor more high-resolution examination tools 102 and/or other examinationtools. The examination tools are configured to capture images and/or toreview the captured image(s) and/or to enable or provide measurementsrelated to the captured image(s). System 103 can be further operativelyconnected to CAD server 110 and data repository 109.

System 103 includes a processor and memory circuitry (PMC) 104operatively connected to a hardware-based input interface 105 and to ahardware-based output interface 106. PMC 104 is configured to provideall processing necessary for operating the system 103 as furtherdetailed hereinafter (see methods described in FIGS. 3 to 5, which canbe performed at least partially by system 103) and includes a processor(not shown separately) and a memory (not shown separately). Theprocessor of PMC 104 can be configured to execute several functionalmodules in accordance with computer-readable instructions implemented ona non-transitory computer-readable memory comprised in the PMC. Suchfunctional modules are referred to hereinafter as comprised in the PMC.Functional modules comprised in PMC 104 include a deep neural network(DNN) 112. DNN 112 is configured to enable data processing using amachine learning algorithm for outputting application-related data basedon the images of specimens.

By way of non-limiting example, the layers of DNN 112 can be organizedin accordance with Convolutional Neural Network (CNN) architecture,Recurrent Neural Network architecture, Recursive Neural Networksarchitecture, Generative Adversarial Network (GAN) architecture, orotherwise. Optionally, at least some of the layers can be organized in aplurality of DNN sub-networks. Each layer of the DNN can includemultiple basic computational elements (CE), typically referred to in theart as dimensions, neurons, or nodes.

Generally, computational elements of a given layer can be connected withCEs of a preceding layer and/or a subsequent layer. Each connectionbetween a CE of a preceding layer and a CE of a subsequent layer isassociated with a weighting value. A given CE can receive inputs fromCEs of a previous layer via the respective connections, each givenconnection being associated with a weighting value which can be appliedto the input of the given connection. The weighting values can determinethe relative strength of the connections and thus the relative influenceof the respective inputs on the output of the given CE. The given CE canbe configured to compute an activation value (e.g. the weighted sum ofthe inputs) and further derive an output by applying an activationfunction to the computed activation. The activation function can be, forexample, an identity function, a deterministic function (e.g., linear,sigmoid, threshold, or the like), a stochastic function, or othersuitable function. The output from the given CE can be transmitted toCEs of a subsequent layer via the respective connections. Likewise, asabove, each connection at the output of a CE can be associated with aweighting value which can be applied to the output of the CE prior tobeing received as an input of a CE of a subsequent layer. Further to theweighting values, there can be threshold values (including limitingfunctions) associated with the connections and CEs.

The weighting and/or threshold values of DNN 112 can be initiallyselected prior to training, and can be further iteratively adjusted ormodified during training to achieve an optimal set of weighting and/orthreshold values in a trained DNN. After each iteration, a difference(also called loss function) can be determined between the actual outputproduced by DNN 112 and the target output associated with the respectivetraining set of data. The difference can be referred to as an errorvalue. Training can be determined to be complete when a cost or lossfunction indicative of the error value is less than a predeterminedvalue, or when a limited change in performance between iterations isachieved. Optionally, at least some of the DNN subnetworks (if any) canbe trained separately, prior to training the entire DNN.

System 103 is configured to receive, via input interface 105, inputdata. Input data can include data (and/or derivatives thereof and/ormetadata associated therewith) produced by the examination tools and/ordata produced and/or stored in one or more data repositories 109 and/orin CAD server 110 and/or another relevant data depository. It is notedthat input data can include images (e.g. captured images, images derivedfrom the captured images, simulated images, synthetic images, etc.) andassociated numeric data (e.g. metadata, hand-crafted attributes, etc.).It is further noted that image data can include data related to a layerof interest and/or to one or more other layers of the specimen.

System 103 is further configured to process at least part of thereceived input data and send, via output interface 106, the results (orpart thereof) to a storage system 107, to examination tool(s), to acomputer-based graphical user interface (GUI) 108 for rendering theresults and/or to external systems (e.g. Yield Management System (YMS)of a FAB). GUI 108 can be further configured to enable user-specifiedinputs related to operating system 103.

By way of non-limiting example, a specimen can be examined by one ormore low-resolution examination machines 101 (e.g. an optical inspectionsystem, low-resolution SEM, etc.). The resulting data (referred tohereinafter as low-resolution image data 121), informative oflow-resolution images of the specimen, can be transmitted—directly orvia one or more intermediate systems—to system 103. Alternatively oradditionally, the specimen can be examined by a high-resolution machine102 (e.g. a subset of potential defect locations selected for review canbe reviewed by a scanning electron microscope (SEM) or Atomic ForceMicroscopy (AFM)). The resulting data (referred to hereinafter ashigh-resolution image data 122) informative of high-resolution images ofthe specimen can be transmitted—directly or via one or more intermediatesystems—to system 103.

It is noted that images of a desired location on a specimen can becaptured at different resolutions. By way of non-limiting example,so-called “defect images” of the desired location are usable todistinguish between a defect and a false alarm, while so-called “classimages” of the desired location are obtained with higher resolution andare usable for defect classification. In some embodiments, images of thesame location (with the same or different resolutions) can compriseseveral images registered therebetween (e.g. images captured from thegiven location and one or more reference images corresponding to thegiven location).

It is noted that image data can be received and processed together withmetadata (e.g. pixel size, text description of defect type, parametersof image capturing process, etc.) associated therewith.

Upon processing the input data (e.g. low-resolution image data and/orhigh-resolution image data, optionally together with other data as, forexample, design data, synthetic data, etc.), system 103 can send theresults (e.g. instruction-related data 123 and/or 124) to any of theexamination tool(s), store the results (e.g. defect attributes, defectclassification, etc.) in storage system 107, render the results via GUI108 and/or send to an external system (e.g. to YMS).

Those versed in the art will readily appreciate that the teachings ofthe presently disclosed subject matter are not bound by the systemillustrated in FIG. 1; equivalent and/or modified functionality can beconsolidated or divided in another manner and can be implemented in anyappropriate combination of software with firmware and/or hardware.

Without limiting the scope of the disclosure in any way, it should alsobe noted that the examination tools can be implemented as inspectionmachines of various types, such as optical imaging machines, electronbeam inspection machines and so on. In some cases the same examinationtool can provide low-resolution image data and high-resolution imagedata. In some cases at least one examination tool can have metrologycapabilities.

It is noted that the examination system illustrated in FIG. 1 can beimplemented in a distributed computing environment, in which theaforementioned functional modules shown in FIG. 1 can be distributedover several local and/or remote devices, and can be linked through acommunication network. It is further noted that in other embodiments atleast some examination tools 101 and/or 102, data repositories 109,storage system 107 and/or GUI 108 can be external to the examinationsystem 100 and operate in data communication with system 103 via inputinterface 105 and output interface 106. System 103 can be implemented asstand-alone computer(s) to be used in conjunction with the examinationtools. Alternatively, the respective functions of the system can, atleast partly, be integrated with one or more examination tools.

Attention is now drawn to FIG. 2, which depicts a method of determininglocations of interest in a semiconductor specimen. The method includes(operation 200) obtaining data representative of a defect location.According to some embodiments, the data can include an image of thesemiconductor specimen acquired by an optical examination tool (see e.g.reference 101). In particular, according to some embodiments, adifference image between the optical image of the semiconductorspecimen, and a reference image, can be generated, and locations of thedifference image in which a difference in pixel intensity is above athreshold can be indicative of a location of interest (which can includea defect). As mentioned above, in various applications the opticalsignal is of low resolution, and therefore the difference image isgenerally not sufficient by itself to detect any defects. In someembodiments, the reference image is an image of another specimen, whichis of the same type as the specimen under examination (indeed,distribution of defects can be modelled as a random distribution,therefore if a significant difference in pixel intensity exists betweentwo specimens of the same type, this can be indicative of a defect). Insome embodiments, the reference image is an image (simulated image ortrue image) of a specimen which is assumed to be without defects.

According to some embodiments, the data can include historical dataregarding defect location (in particular for the type of specimen whichis under examination). According to some embodiments, the data caninclude an image of the specimen acquired by an optical examination tool(see e.g. reference 101). Indeed, in some embodiments, although theoptical examination tool does not provide an image with sufficientresolution to detect defects (e.g. in case of small structures), dataprovided by the optical examination tool can still include a firstestimation of areas for which presence of defect(s) is suspected(thereby reducing the size of the areas to be considered in subsequentoperations of the method of detecting detects). In some embodiments,data provided by the optical examination tool can correspond to imagesacquired using different wavelengths of the illumination beam. Accordingto some embodiments, the data can include an image of the semiconductorspecimen acquired by an electron beam examination tool (see e.g.reference 102). According to some embodiments, the data can includecritical dimension uniformity (CDU) over one or more areas of thesemiconductor specimen. During the fabrication process, a plurality ofpattern features are formed in the substrate. Critical dimension (CD)includes e.g. gate width, the minimum width of a line, or the minimumspace between two lines permitted in the manufacturing of the specimen.CDU characterizes variations of the critical dimension in an area of thespecimen.

According to some embodiments, the data can include a simulated image ofthe semiconductor specimen. In particular, this can include design data,such as CAD data (provided e.g. by a user), which can includeinformation regarding structured elements present in the specimen forwhich likelihood of presence of a defect is high (“CAD hotspot(s)”).According to some embodiments, the data can include a synthetic image ofthe semiconductor specimen. According to some embodiments, the data caninclude manufacturing data of the semiconductor specimen (e.g.temperature, pressure, type of gas, type of manufacturing tools).Indeed, manufacturing data can influence location of defects in thesemiconductor specimen. Generally, the impact of variations ofmanufacturing data on the specimen has a dimension which is larger thanthe resolution of an optical examination tool.

The method can include using (210) the data to build a first probabilitymap representing probability of a presence of defects over the specimen.In some embodiments, the data can be fed to a trained deep neuralnetwork (such as DNN 112), which can output, for each pixel, or area ofpixels of the specimen, a first probability that a defect is present.The deep neural network can be pre-trained using supervised learning toprovide the required map. During supervised learning, a label providedby an operator and indicative of the presence of defects is usedtogether with a training set including data as obtained in operation200. Use of a deep neural network to generate the first probability mapis only a possible example, and other methods can be used, such asphysical modelling (which involves the use of one or more statisticalmodels). Based on this first probability map, it is possible to obtain afirst estimate of the locations of interest in the specimen (theselocations can correspond to locations in the first probability map forwhich the probability of presence of a defect is above a threshold),which should be further examined.

According to some embodiments, a plurality of images of the specimen canbe acquired by an examination tool (such as optical examination tool101), wherein the images differ by at least one acquisition parameter,such as the wavelength. As explained above, a difference image can begenerated between the image of the specimen and a reference image. Adifference image for each wavelength, together with other datarepresentative of location of defects, can be used to build a firstprobability map per wavelength. In some embodiments, an aggregated firstprobability map can be built (e.g. by taking the maximum value for theprobability for all wavelengths—other aggregations can be used toaggregate the plurality of first probability maps).

Attention is now drawn to FIG. 3. A method can include obtaining (305)an image of a first area of a specimen acquired by an examination tool.In some embodiments, the examination tool can be a high-resolutionexamination tool (see reference 102 in FIG. 1). According to someembodiments, the first area can be selected based on the firstprobability map that has been computed according to the method of FIG.2. In particular, the first area can be selected as an area for whichthe probability map indicates that the probability of a presence of adefect is above a threshold.

According to some embodiments, operation 305 can include obtaining animage of each of a plurality of first areas of a semiconductor specimenacquired by an examination tool (such as examination tool 102). Theplurality of first areas can be selected based on the probability mapcomputed according to the method of FIG. 2. According to someembodiments, the plurality of first areas are selected such that theyare sufficiently spread (in particular along the radial direction of thespecimen). According to some embodiments, the plurality of first areasare selected such that thickness evolution of the specimen is differentbetween at least some of the first areas, and/or such that thickness ofthe specimen in at least some of the first areas evolves and is notconstant (as explained hereinafter, pixel intensity in an optical imageis correlated to thickness and can be used to characterize thickness ofthe specimen). This allows increasing diversification of the informationthat is processed.

For a given first area, the method can include (310) determining dataD_(att) informative of defectivity (that is to say data informative of apresence of defect(s)) in the given area. Various examples of such datafor one or more attributes are provided hereinafter. As explainedhereinafter, D_(att) can be particular in that defects tend to bepresent for values of D_(att) which correspond to an extremum ofD_(att).

According to some embodiments, data D_(att) in each of the one or morefirst areas is determined based on a plurality of optical images of theone or more first areas. The plurality of optical images can differ bythe wavelength of the illuminating optical signal. Therefore, for eachwavelength, different data D_(att) can be obtained.

Data informative of defectivity in the one or more first areas can beobtained using various methods.

According to some embodiments, data representative of a shape ofelements (e.g. structured elements such as contacts, transistors)present in the one or more first areas can be obtained. This can includee.g. data representative of the contour of the elements (e.g. line edgeroughness LER, which refers to the non-smoothness of edges of elementspresent in the specimen), data representative of the surface of theelements, data representative of the perimeter of the elements, and datarepresentative of the size of the elements. In some embodiments, thisdata can include critical dimension uniformity (CDU).

This various data can be obtained based on optical images previouslyacquired by an optical examination tool (such as examination tool 101)and/or based on the image acquired by the examination tool (which can bein particular examination tool 102) at operation 305. This data can becompared to reference data (design data such as CAD data, or to areference image of a specimen without defects). If the difference isabove a threshold, this can be indicative of the presence of a defect.

More generally, data representative of a difference between structuredelements present in the one or more first areas, and structured elementspresent in a reference image of the given first area, can be used todetect defects. This can include e.g. a difference in the position (ororientation) of the structured elements in the one or more first areaswith respect to a reference image of the one or more first areas.

In some embodiments, evolution of the data representative of a shape ofthe structured elements can be analysed within the one or more firstareas. For example, if all structured elements have a similar shape(e.g. circular shape), and one structured element has a shape which isbecoming different from a common average shape, or already has adifferent shape (e.g. ellipse), then this can be indicative of a defect.More generally, for other attributes which can be indicative of adefect, distribution of the values of the attribute in the one or morefirst areas can be computed, and deviation from a mean value can bedetected.

The method can further include determining (320) one or more secondareas of the semiconductor specimen for which presence of a defect issuspected, wherein a given second area is determined based at least onD_(att) in the one or more first areas (or data correlated to D_(att),as explained hereinafter).

Generally, data D_(att) informative of defectivity (such as CDU, LER,etc.) is such that there is an extremum (which can be either a maximumor a minimum, or both, depending on the attribute) for which theprobability to find a defect is the highest. It can be thereforeattempted to find this extremum, or at least to tend towards thisextremum. Since a plurality of values of D_(att) has been obtained (e.g.for one or more areas over the specimen), the second area can beselected to attempt to move towards an extremum of D_(att). According tosome embodiments, selection of the one or more second areas can rely onmethods such as (but not limited to) Gradient descent method, Newton'smethod, explore/exploit algorithms.

A simple (and non-limitative) example is illustrated in FIG. 3A. Assumethat D_(att) has been obtained for a given attribute informative ofdefectivity, for nine different areas of the specimen (areas 331 to339). Assume that D_(att) has a constant value for areas 334 to 339, butthat value of D_(att) decreases from area 331 to area 332, and increasesagain from area 332 to area 333. Therefore, if it is known that for thespecific attribute for which D_(att) has been computed, a minimum valuecorresponds to a high probability of finding a defect, this indicatesthat further acquisitions should be performed between areas 331 and 332.In particular, the method can be repeated, in order to acquire morevalues of D_(att) at areas 340 and 341 (finer sampling), until a defectis found.

It has been mentioned with reference to FIG. 2 that a first probabilitymap can be built, which indicates locations of interest for which thereis a (serious) likelihood that a defect is present. According to someembodiments, operation 320 can include updating the first probabilitymap based on D_(att). In particular, areas for which it is expected thatD_(att) has a value which corresponds to an extremum (based on the trendof D_(att) discovered at operation 310 in the one or more first areas)can get an increase of their probability, whereas other areas can get adecrease of their probability. The one or more second areas can bedetermined and selected based on the updated first probability map.

According to some embodiments, D_(att) is determined separately atoperation 310 in the one or more first areas for each of a plurality ofdifferent attributes (for example, D_(att,1) corresponds to CDU,D_(att,2) corresponds to LER, etc.). Each attribute can follows its owntrend on the wafer. As a consequence, for each attribute, a differentprobability can be obtained that at a given location, a defect ispresent. An aggregated probability P can be computed for each pixel orarea of pixels, e.g. by multiplying all probabilities determined for allattributes (this is not limitative, and other statistical formula can beused): P=ΠP_(attribute), wherein P_(attribute) is the probability offinding a defect for a given attribute. In some embodiments, sinceD_(att) can be computed for a plurality of different wavelengths, aplurality of probabilities that a defect is present at a given locationcan be obtained. As a consequence, an aggregated probability can begenerated, using any adapted statistical formula, as explained above.

Once the one or more second areas have been identified, they can beselected (operation 330) for inspection by the examination tool. Inparticular, according to some embodiments, operation 330 can includegenerating an instruction for the examination tool, which instructs theexamination tool to acquire an image of the one or more second areas.Based on the image of the one or more second areas, it is possible todetect whether the second area includes a defect. For example, if theexamination tool is a high-resolution examination tool (such as anelectron beam microscope 102), then it can output a clear-cut decisionon whether or not a defect is present.

According to some embodiments, the method of FIG. 3 can be repeatediteratively, until a stopping criterion is met. This is illustrated inFIG. 4.

The method includes selecting (operation 400) at least one area A_(i) ofthe semiconductor specimen for inspection by an examination tool andobtaining (operation 405) an image of the at least one area A_(i)acquired by the examination tool. Operation 405 is similar to operation305. At the first iteration (for i=1), A_(i) can be selected based onthe first probability map computed as explained with reference to FIG.2.

The method includes determining (operation 410) data D_(att) informativeof defectivity in the one or more areas A_(i). Operation 410 is similarto operation 310. The method can include, in some embodiments, updating(415) the probability map (currently in use) based on D_(att).

The method includes determining (operation 420) one or more areasA_(i+1). This can be performed based e.g. on the probability map (and/orbased on D_(att)). Operation 420 can be similar to operation 320. Theone or more areas A_(i+1) can then be selected for inspection by theexamination tool.

The method can then be repeated (see reference 440). At the nextiteration, D_(att) of the one or more areas A_(i+1) is computed andhelps to determine whether the trend determined for D_(att) in the oneor more areas A_(i) pursues in the newly selected area(s) A_(i+1).

According to the method of FIG. 4, a first rough sampling is thereforeperformed to detect a general trend of D_(att) (for one or moreattributes) over the specimen, and, during subsequent iterations, afiner sampling of the values of D_(att) is performed on specific areasof the specimen, in order to attempt to tend towards an extremum(minimum and/or maximum depending on the attribute) of D_(att), forwhich there is a high probability to find a defect.

According to some embodiments, update of the probability map atoperation 415 (to obtain updated probability P_(new) for each pixel orareas of pixels of the probability map) can include performing aweighted combination between previous probability (P_(prev) obtained atprevious iteration “i”) and current probability (P_(curr) obtained atiteration “i+1”). According to some embodiments, the following formulacan be used (this is not limitative—α is a weight selected e.g. by anoperator):

P _(new) =αP _(prev)+(1−α)P _(curr)

During iterations of the method of FIG. 4, it is expected that, at somestage, output of the examination tool indicates presence of a defect ina selected area A_(k) (for some value of k). In other words, use ofD_(att) has been fruitful to converge and find a defect.

Attention is now drawn to FIG. 5, which describes another possibleimplementation of the method of FIG. 3. In particular, in FIG. 3, it hasbeen mentioned that selection of the one or more second areas can beperformed based on data correlated to D_(att). FIG. 5 illustrates apossible embodiment of this method.

The method includes obtaining an image of at least one given first area(or a plurality of given first areas) of a specimen (operation 505,similar to operation 305).

The method further includes determining (operation 510) data D_(correl)representative of a correlation between data D_(att) informative ofdefectivity in the given first area, and data representative of athickness of the specimen in the given first area. In some embodiments,operation 310 can be performed on a plurality of the given first areas,or on all of the given first areas. Various examples have already beenprovided for D_(att) and can be used in this method.

According to some embodiments, data representative of a thickness of thespecimen in the given first area can be obtained in particular based onan image of the given first area acquired by an optical examination tool(see e.g. reference 101). Indeed, the thickness of the specimen (whichcan include one or more layers) has an impact on the diffraction of theoptical signal, and therefore, on the pixel intensity in the opticalimage. Depending on the wavelength of the illuminating optical signal,in some cases, the higher the thickness of the specimen, the higher theintensity (“grey level”) of the corresponding pixel(s) in the opticalimage (positive correlation), and in other cases, the lower thethickness of the specimen, the higher the intensity (“grey level”) ofthe corresponding pixel(s) in the optical image (negative correlation).

Since pixel intensity in an optical image of the specimen is correlatedto the thickness of the specimen, the pixel intensity can be used asdata representative of the thickness of the specimen. It is notnecessary to determine the relationship between pixel intensity and thethickness of the specimen, since this relationship is not necessaryknown or available: it is sufficient to know that the pixel intensity inthe optical image is representative of the thickness of the specimen.

According to some embodiments, data representative of a thickness of thespecimen in the given first area is determined based on a plurality ofoptical images of the given first area. This is illustrated in FIG. 5A.The plurality of optical images (acquired at operation 340) can differby the wavelength of the illuminating optical signal. Distribution ofthe pixel intensity of the given first area in each of the plurality ofoptical images is generally different. Indeed, since each optical signalhas a different wavelength, a different level of diffraction is obtainedand therefore, for each wavelength, a different pixel intensitydistribution is obtained, each representative of the thicknessdistribution.

As mentioned above, operation 510 includes determining data D_(correl)between first data (data informative of defectivity in the given firstarea) and second data (data representative of a thickness of thesemiconductor specimen in the given first area—in practice, as mentionedabove, pixel intensity in an optical image can be used to characterizethickness of the specimen). Indeed, it is expected that there is acorrelation between the thickness of the specimen and the probabilitythat a defect is present. This correlation has been observedexperimentally.

Assume that data D_(att) informative of defectivity in the given firstarea is represented by variable Z, pixel intensity in the optical imageis represented by variable Y, and data representative of a thickness ofthe semiconductor specimen in the given first area is represented byvariable X. Determining data D_(correl) can include determining afunction F, such as Z=F(Y). Since it is known that there Y is correlatedto X, this is equivalent to determining Z=G(X). As explainedhereinafter, for some types of attributes Z, the higher the value of Z,the higher the probability of finding a defect, and for other types ofattributes Z, the lower the value of Z, the higher the probability offinding a defect.

According to some embodiments, F can be determined using e.g. methodssuch as interpolation (a non-limitative example includes linearregression), or other statistical methods. Assume that for a givenattribute representative of a defect in the given first area (e.g. CDU),a plurality of pairs of values can be obtained (Z=value of theattribute; Y=pixel intensity). As a consequence, a function F can beobtained for this attribute. According to some embodiments, for eachattribute representative of a defect, a function F_(attribute) can bedetermined separately.

The method can further include determining (520) one or more secondareas of the semiconductor specimen for which presence of a defect issuspected, wherein the one or more second areas are determined based onD_(correl) (or data representative thereof, such as a defect probabilitygenerated based on D_(correl)) and data representative of a thickness ofthe specimen. In practice, as mentioned above, value of the thickness isgenerally not directly available, and the pixel intensity in an opticalimage can be used to represent thickness.

Indeed, F (or F_(attribute)), which are part of D_(correl), can be usedto determine for which values of the pixel intensity (and in turn of thethickness) and/or for which evolution of the pixel intensity (and inturn of the thickness), there is a probability that a defect is presentat the given location. For example, if the attribute is such that a highvalue of the attribute corresponds to a high probability of finding adefect, then the pixel intensity associated with the high value ofF_(attribute) can be determined, and can be used to select the secondarea. For example, if the attribute is CDU, it is expected that thehigher the value of CDU, the higher the probability of finding a defect.This is not limitative, and for other attributes, a low value of theattribute can correspond to a high probability of finding a defect.

Since data representative of a thickness of the specimen is available(e.g. based on an optical image of the specimen), and D_(correl)characterizes the relationship between the thickness of the specimen(through the pixel intensity) and data informative of defectivity, thesecond area can be selected as an area for which pixel intensity (andtherefore thickness of the specimen) is expected to reflect the presenceof a defect. The second area can be selected (operation 530) for beinginspected by the examination tool, in order to confirm whether thesecond area includes a defect.

It has been mentioned with reference to FIG. 2 that a first probabilitymap can be built, which indicates locations of interest for which thereis a (serious) likelihood that a defect is present. According to someembodiments, operation 520 can include updating the first probabilitymap based on D_(correl) and data representative of a thickness of thespecimen. In other words, D_(correl) can be used to compute aprobability that a defect is present (even if the area has not beenacquired by the examination tool 102). In particular, areas which have apixel intensity (e.g. value of the pixel intensity, and/or pixelintensity evolution) for which D_(correl) indicates a high probabilitythat a defect is present, will get an increase of their probability,whereas areas which have a pixel intensity for which D_(correl)indicates a low probability that a defect is present, will get adecrease of their probability. The one or more second areas can bedetermined and selected based on the updated probability map.

For example, assume that D_(correl) indicates a high probability ofdefects for specific values of pixel intensity (e.g. in range [Y₁;Y₂]).Then, the second area can be selected such that its pixel intensity islocated in this range. In some embodiments, the pixels (which belong tothe first probability map) can be clustered into a plurality of clustersbased on pixel intensity, and therefore, the cluster of pixels which isthe closest to the selected range of pixel intensity will get a highprobability.

As mentioned above, according to some embodiments, a function F (orF_(attribute)) representative of the correlation between value of theattribute (representative of location of a defect) and pixel intensityis determined for each of a plurality of attributes. Update of the firstprobability map can include determining, for each pixel or area ofpixels of the map, a probability P_(attribute) that a defect is presentbased on F_(attribute) and on the pixel intensity (representative of thethickness of the specimen). A non-limitative example of modelling of theprobability can include:

$P_{attribute}\left( {{Y\left. {\mu,\sigma} \right)} = {\frac{1}{\sqrt{2\pi}\sigma}{e^{- \frac{{({Y - \mu})}^{2}}{2\sigma^{2}}}.}}} \right.$

In this expression, Y is the pixel intensity in an optical image(representative of the thickness). μ can be determined based onF_(attribute) obtained for this attribute. For example, if F_(attribute)indicates that for a given value Y* of the pixel intensity, theprobability to find a defect is high (and decreases when moving awayfrom Y*), then μ can be selected such that μ=Y*.

An aggregated probability P can be computed for each pixel or area ofpixels, e.g. by multiplying all probabilities determined for allattributes (this is not limitative, and other statistical formula can beused): P=⊂P_(attribute).

According to some embodiments, the second area can be selected in orderto test the function F (or P_(attribute)) obtained at the previousiteration (for the first area). For example, if the function F indicateshigh probability of defect in a range R of pixel intensity(representative of thickness), then the second area can be selected witha pixel intensity in this range. In other examples, if the function Findicates high probability of defect in a range R of pixel intensity(representative of thickness), then the second area can be selected witha pixel intensity in a range R′ different from this range R (e.g. R′ caninclude R).

As explained above, generally, data indicative of a presence of a defect(such as CDU, LER, etc.) is such that there is an extremum (which can beeither a maximum and/or a minimum, depending on the attribute) for whichthe probability to find a defect is the highest. It can be thereforeattempted to find this extremum, or at least to tend towards thisextremum. The second area can be selected to attempt to move towards anextremum of an output of the function F. For example, for CDU, it isexpected that CDU will have a high value in a defective area, andtherefore a maxima is to be found.

Assume that the function F indicates a high probability of presence of adefect (reflected by the value of Z=F(Y)) when pixel intensity increasesfrom Y₁ to Y₁+5 (see the example of FIG. 5B—in this example, the higherthe value of the attribute, the higher the probability of a defect). Itcan be attempted to check whether this trend is indeed representative ofa defect, by selecting a second area for which pixel intensity evolvesfrom Y₁+5 to Y₁+10 and/or by selecting an area for which the pixelintensity is already equal to Y₁+10. In other words, it is attempted tocheck whether the trend identified at the previous iteration is correct,in order to hopefully reach an extremum (in FIG. 5B, Z_(max)) of Z.

In some embodiments, probability assigned to the pixel areas (in theprobability map as mentioned above) for which the pixel intensityfollows the relationship mentioned above can be increased, therebyincreasing prospects that these areas will be selected at the nextiteration.

Similarly, assume that the function F for another attribute indicates ahigh probability of presence of a defect (reflected by the value ofZ=F(Y)) when the pixel intensity decreases from Y₁ to Y₁−5 (see FIG.5C—in this example, the lower the value of the attribute, the higher theprobability of a defect). It can be attempted to check whether thistrend is indeed representative of a defect, by selecting a second areafor which the pixel intensity evolves from Y₁−5 to Y₁−10, and/or byselecting an area for which the thickness is already equal to Y₁−10. Inother words, it is attempted to check whether the trend identified atthe previous iteration is correct, in order to hopefully reach anextremum of Z (in FIG. 5C, Z_(min)).

According to some embodiments, selection of the second area (in order tocheck validity of the correlation function determined at the previousiteration of the method) can rely on methods such as Gradient descentmethod, Newton's method, explore/exploit algorithms (explore/exploitalgorithms are particularly useful when the function includes aplurality of local extrema).

It has been mentioned with reference to FIG. 5A that according to someembodiments, a plurality of images which differ by at least oneacquisition parameter (such as wavelength) can be obtained. Therefore,for each acquisition parameter, a different distribution of pixelintensity is obtained, and D_(correl) can be computed independently foreach acquisition parameter. According to some embodiments, a differentprobability map can be obtained for each acquisition parameter, and thesecond area can be selected e.g. as the area for which the probabilityis the highest among all different probability maps.

Once the second area has been identified, it can be selected (operation530) for inspection by the examination tool, as explained with referenceto operation 330.

According to some embodiments, the method of FIG. 5 can be repeatediteratively, until a stopping criterion is met. This is illustrated inFIG. 6.

The method includes selecting (operation 600) at least one area A_(i) ofthe semiconductor specimen for inspection by an examination tool andobtaining (operation 605) an image of the at least one area A_(i)acquired by the examination tool. Operation 605 is similar to operation505. At the first iteration (for i=1), A_(i) can be selected based onthe first probability map computed as explained with reference to FIG.2.

The method includes determining (operation 610) data D_(correl,i)representative of a correlation between data D_(att) informative ofdefectivity in the area A_(i), and data representative of a thickness ofthe specimen in the area A_(i) (in particular, pixel intensity isrepresentative of a thickness of the specimen in the area A_(i)).Operation 610 is similar to operation 610. The method can include, insome embodiments, updating (615) the probability map (currently in use)based on D_(correl,i).

The method includes determining (operation 620) at least one areaA_(i+1). This can be performed based e.g. on the probability map (and/orbased on D_(correl,i) and on data representative of a thickness of thespecimen). Operation 620 can be similar to operation 520. The areaA_(i+1) can then be selected for inspection by the examination tool.

The method can then be repeated (see reference 640). At the nextiteration, D_(correl,i+1) of area A_(i+1) is computed and helps todetermine whether the trend (and/or specific values of pixel intensity)determined in D_(correl,i) as representative of a defect, is correct.

For example, assume that D_(correl,i) indicates that for a range ofpixel intensity [Y₁;Y₂] there is a high probability of finding a defect,and that D_(correli,i+1) confirms this hypothesis. Then the probabilitymap can be updated (operation 615) to further increase the probabilityassigned to areas of pixels which have a thickness located in thisrange.

In another example, assume that D_(correl,i) indicates that for a rangeof pixel intensity [Y₃;Y₄] there is a high probability of finding adefect, and that D_(correll,i+1) contradicts this hypothesis. Then theprobability map can be updated (operation 615) to reduce the probabilityassigned to areas of pixels which have a pixel intensity located in thisrange.

In another example, assume that D_(correl,i) indicates that a particulargradient or trend in the pixel intensity (and in turn of the thickness)is indicative of a defect. As mentioned above, A_(i+1) can be selectedin order to test whether this gradient or trend in the pixel intensityis actually indicative of a defect.

If D_(correl,i+1) (representative of a correlation between dataindicative of a presence of a defect in the area A_(i+1) and datarepresentative of a thickness of the specimen in the area A_(i+1))confirms that the gradient or trend in the pixel intensity (and in turnof the thickness) as present in D_(correl,i) is indicative of a defect,then the probability map can be updated (operation 615) to increaseprobability assigned to areas of pixels which have a pixel intensitywhich follows the gradient or trend as described in D_(correl,i) andD_(correl,i+1).

If D_(correl,i+1) (representative of a correlation between dataindicative of a presence of a defect in the area A_(i+1) and datarepresentative of a thickness of the specimen in the area A_(i+1))indicates that the gradient or trend in the pixel intensity as presentin D_(correl,i) is not indicative of a defect, then the probability mapcan be updated (operation 615) to reduce probability assigned to areasof pixels which have a pixel intensity which follows a gradient or trendas described in D_(correl,i). The correct gradient or trend indicativeof a defect can be found e.g. after several subsequent iterations.

According to some embodiments, update of the probability map atoperation 615 (to obtain updated probability P_(new) for each pixel orareas of pixels of the probability map) can include performing aweighted combination between previous probability (P_(prev) obtained atprevious iteration “i”) and current probability (P_(curr) obtained atiteration “i+1”), as already explained with reference to FIG. 4.

During iterations of the method of FIG. 6, it is expected that, at somestage, output of the examination tool indicates presence of a defect ina selected area A_(k) (for some value of k). In other words, use ofD_(correl) has been fruitful to converge and find a defect. According tosome embodiments, data D_(Ak) representative of the thickness of thisarea A_(k) can be used to update the probability map. In particular,since a defect has been detected in this area A_(k), the probability mapcan be updated, such that any area of pixels for which pixel intensityincludes values and/or evolves similarly to D_(Ak), (e.g. according to asimilarity criterion, which can include a threshold), is assigned with ahigh probability (e.g. close to one). As a consequence, during a futureiteration of the method (see reference 640 which depicts suchiteration), these areas of pixels will be selected (operation 600) forinspection by the examination tool, thereby increasing the prospects todetermine additional defects.

According to some embodiments, there is a limitation on the number ofareas that can be acquired by the examination tool. This limitation canbe due e.g. to time and/or cost constraints. This limitation isencountered in particular for a high-resolution examination tool, suchas examination tool 102 (e.g. electron beam microscope). As aconsequence, not all areas can be acquired by the examination tool.Since a probability map has been computed based on D_(correl)i and datarepresentative of a thickness of the semiconductor specimen in the oneor more areas, it is possible to output a probability that the one ormore areas include a defect without requiring acquisition of an image ofthe one or more areas by the examination tool. In particular, the one ormore areas for which the probability that a defect is present is above athreshold can be output (although, in practice the high-resolutionexamination tool has not acquired an image of these areas).

According to some embodiments, D_(correl) and/or the probability map canbe determined on a first specimen, and can be used for other specimens(which are similar to the first specimen, e.g. same type of wafer, orsame manufacturing process) to select locations to be examined by theexamination tool (e.g. 102). In some embodiments, locations for which ahigh probability of defects has been found in the first specimen, can beexamined in the subsequent similar specimens. In some embodiments,D_(correl) and/or the probability map is informative of a probabilitythat a defect is present based on pixel intensity (which reflectsthickness evolution). It is therefore possible to determine locations ofinterest in the subsequent similar specimens based on distribution ofpixel intensity, without requiring acquisition by the examination tool(e.g. 102) of the whole specimen.

It is to be understood that the invention is not limited in itsapplication to the details set forth in the description contained hereinor illustrated in the drawings.

It will also be understood that the system according to the inventionmay be, at least partly, implemented on a suitably programmed computer.Likewise, the invention contemplates a computer program being readableby a computer for executing the method of the invention. The inventionfurther contemplates a non-transitory computer-readable memory tangiblyembodying a program of instructions executable by the computer forexecuting the method of the invention.

The invention is capable of other embodiments and of being practiced andcarried out in various ways. Hence, it is to be understood that thephraseology and terminology employed herein are for the purpose ofdescription and should not be regarded as limiting. As such, thoseskilled in the art will appreciate that the conception upon which thisdisclosure is based may readily be utilized as a basis for designingother structures, methods, and systems for carrying out the severalpurposes of the presently disclosed subject matter.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of theinvention as hereinbefore described without departing from its scope,defined in and by the appended claims.

What is claimed is:
 1. A system of examination of a semiconductorspecimen, the system comprising a processor and memory circuitry (PMC)configured to: obtain an image of one or more first areas of asemiconductor specimen acquired by an examination tool, determine dataD_(att) informative of defectivity in the one or more first areas,determine one or more second areas of the semiconductor specimen forwhich presence of a defect is suspected based at least on an evolutionof D_(att), or of data correlated to D_(att), in the one or more firstareas, and select the one or more second areas for inspection by theexamination tool.
 2. The system of claim 1, wherein determination of theone or more second areas is based on a search of an extremum of D_(att).3. The system of claim 1, configured to: determine D_(correl)representative of a correlation between: data D_(att) informative ofdefectivity in the one or more first areas, and data representative of athickness of the semiconductor specimen in the one or more first areas,determine one or more second areas of the semiconductor specimen forwhich presence of a defect is suspected, wherein the one or more secondareas are determined based on D_(correl), or data representativethereof, and data representative of a thickness of the semiconductorspecimen, and select the one or more second areas for inspection by theexamination tool.
 4. The system of claim 1, configured to performrepeatedly (1), (2), (3) and (4), from i equal to 1, until a stoppingcriterion is met: (1) obtain an image of one or more areas A_(i) of asemiconductor specimen acquired by an examination tool, (2) determinedata D_(att) informative of defectivity in the one or more areas A_(i),(3) determine one or more areas A_(i+1) of the semiconductor specimenfor which presence of a defect is suspected, based at least on anevolution of D_(att) in the one or more areas A_(i), and (4) revertingto (1) for i incremented by one.
 5. The system of claim 1, configured toperform repeatedly (1), (2), (3) and (4), from i equal to 1, until astopping criterion is met: (1) obtain an image of an area A_(i) of asemiconductor specimen acquired by an examination tool, (2) determinedata D_(correl,i) representative of a correlation between: data D_(att)informative of defectivity in the area A_(i), and data representative ofa thickness of the semiconductor specimen in the area A_(i), (3)determine one or more areas A_(i+1) of the semiconductor specimen forwhich presence of a defect is suspected, wherein the one or more areasA_(i+1) are determined based at least on D_(correl,i), or datarepresentative thereof, and data representative of a thickness of thesemiconductor specimen, and (4) reverting to (1) for i incremented byone.
 6. The system of claim 1, configured to generate for each of aplurality of subsets of pixels present in the image of the semiconductorspecimen, a probability that a defect is present at each subset, whereinthe second area is selected based at least on the probability, whereinthe probability is based on at least one of and (i) and (ii): (i) dataD_(att) informative of defectivity in the one or more first areas; and(ii) data D_(correl) representative of a correlation between dataD_(att) informative of defectivity in the one or more first areas anddata representative of a thickness of the semiconductor specimen in theone or more first areas, and data representative of a thickness of thesemiconductor specimen.
 7. The system of claim 1, wherein data D_(att)informative of defectivity in the one or more first areas includes atleast one of: data representative of a shape of elements present in theone or more first areas, and data representative of a difference betweenelements present in the one or more first areas and elements present ina reference image of the one or more first areas.
 8. The system of claim3, wherein if data D_(correl) includes a function F which depends ondata representative of a thickness of the semiconductor specimen over arange R, the system is configured to select the second area such that atleast one of (i), (ii) and (iii) is met: (i) data representative of athickness of the semiconductor specimen in the second area complies withthe function F in the range R; (ii) data representative of a thicknessof the semiconductor specimen in the second area allows testing thefunction F over a range R′ different from R; and (iii) datarepresentative of a thickness of the semiconductor specimen in thesecond area is selected to attempt to move towards an extremum of anoutput of the function F.
 9. The system of claim 3, wherein at least oneof data representative of a thickness of the semiconductor specimen inthe one or more first areas, and data representative of a thickness ofthe semiconductor specimen, is obtained based on pixel intensity in animage acquired by at least one optical examination tool.
 10. The systemof claim 3, wherein at least one of data representative of a thicknessof the semiconductor specimen in the one or more first areas and datarepresentative of a thickness of the semiconductor specimen is obtainedbased on pixel intensity in a plurality of images acquired by at leastone optical examination tool, wherein the plurality of images differ bya wavelength of an illuminating optical signal of the opticalexamination tool.
 11. The system of claim 1, configured to select theone or more first areas based on a first probability map representingprobability of a presence of defects over the semiconductor specimen,wherein the first probability map is built based on at least one of: animage of the semiconductor specimen acquired by an optical examinationtool; estimation of defect location based on an image of thesemiconductor specimen acquired by an optical examination tool;historical data regarding defect location; an image of the semiconductorspecimen acquired by an electron beam examination tool; a simulatedimage of the semiconductor specimen, a synthetic image of thesemiconductor specimen, and manufacturing data of the semiconductorspecimen.
 12. The system of claim 3, configured to output datarepresentative of a probability that a defect is present in one or moreareas of the semiconductor specimen, or of another semiconductorspecimen, based on data representative of a thickness of thesemiconductor specimen, or the another semiconductor specimen, in theone or more areas, without requiring acquisition of an image of the oneor more areas by the examination tool.
 13. A method of examination of asemiconductor specimen, the method comprising, by a processor and memorycircuitry (PMC): obtaining an image of one or more first areas of asemiconductor specimen acquired by an examination tool, determining dataD_(att) informative of defectivity in the one or more first areas,determining one or more second areas of the semiconductor specimen forwhich presence of a defect is suspected based at least on an evolutionof D_(att), or of data correlated to D_(att), in the one or more firstareas, and selecting the one or more second areas for inspection by theexamination tool.
 14. The method of claim 13, wherein determination ofthe one or more second areas is based on a search of an extremum ofD_(att).
 15. The method of claim 13, including performing repeatedly(1), (2), (3) and (4), from i equal to 1, until a stopping criterion ismet: (1) obtaining an image of one or more areas A_(i) of asemiconductor specimen acquired by an examination tool, (2) determiningdata D_(att) informative of defectivity in the one or more areas A_(i),(3) determining one or more areas A_(i+1) of the semiconductor specimenfor which presence of a defect is suspected, based at least on anevolution of D_(att) in the one or more areas A_(i), and (4) revertingto (1) for i incremented by one.
 16. The method of claim 13, includingperforming repeatedly (1), (2), (3) and (4), from i equal to 1, until astopping criterion is met: (1) obtaining an image of an area A_(i) of asemiconductor specimen acquired by an examination tool, (2) determiningdata D_(correl,i) representative of a correlation between: data D_(att)informative of defectivity in the area A_(i), and, data representativeof a thickness of the semiconductor specimen in the area A_(i), (3)determining one or more areas A_(i+1) of the semiconductor specimen forwhich presence of a defect is suspected, wherein the one or more areasA_(i+1) are determined based at least on D_(correl,i), or datarepresentative thereof, and data representative of a thickness of thesemiconductor specimen; and (4) reverting to (1) for i incremented byone.
 17. The method of claim 13, including generating for each of aplurality of subsets of pixels present in the image of the semiconductorspecimen, a probability that a defect is present at each subset, whereinthe second area is selected based at least on the probability, whereinthe probability is based on at least one of and (i) and (ii): (i) dataD_(att) informative of defectivity in the one or more first areas; and(ii) data D_(correl) representative of a correlation between dataD_(att) informative of defectivity in the one or more first areas anddata representative of a thickness of the semiconductor specimen in theone or more first areas, and data representative of a thickness of thesemiconductor specimen.
 18. The method of claim 13, wherein data D_(att)in the one or more first areas includes at least one of: datarepresentative of a shape of elements present in the one or more firstareas, and data representative of a difference between elements presentin the one or more first areas and elements present in a reference imageof the one or more first areas.
 19. The method of claim 16, wherein atleast one of data representative of a thickness of the semiconductorspecimen in the area A_(i), and data representative of a thickness ofthe semiconductor specimen, is obtained based on pixel intensity in aplurality of images acquired by at least one optical examination tool,wherein the plurality of images differ by a wavelength of anilluminating optical signal of the optical examination tool.
 20. Anon-transitory computer readable medium comprising instructions that,when executed by a processor, cause the processor to perform operationscomprising: obtaining an image of one or more first areas of asemiconductor specimen acquired by an examination tool, determining dataD_(att) informative of defectivity in the one or more first areas,determining one or more second areas of the semiconductor specimen forwhich presence of a defect is suspected based at least on an evolutionof D_(att), or of data correlated to D_(att), in the one or more firstareas, and selecting the one or more second areas for inspection by theexamination tool.